Hoisting process danger identification method and system based on deep learning

A hazard identification and deep learning technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve problems such as inability to effectively prevent hoisting accidents, single hazard identification methods, etc. Comprehensive and practical effect

Pending Publication Date: 2020-04-24
杭州鲁尔物联科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Although the above application can improve the safety during the hoisting process by detecting the distance between the worker and the hook, the hazard identification method for the hoisting process is single, and it is only carried out when the distance between the worker and the hook is detected to be lower than a certain value. Safety warning cannot effectively prevent hoisting accidents

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  • Hoisting process danger identification method and system based on deep learning
  • Hoisting process danger identification method and system based on deep learning
  • Hoisting process danger identification method and system based on deep learning

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Experimental program
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Embodiment 1

[0054] Such as figure 1 As shown, this embodiment proposes a method for hazard identification in the hoisting process based on deep learning, including:

[0055] S1. Training and generating Faster R-CNN workers and hook detection network;

[0056] In order to improve the efficiency of hazard identification in the hoisting process, the present invention uses Faster R-CNN to detect workers and hooks. The Faster RCNN network is one of the more popular general multi-target detection frameworks. The existing Faster RCNN algorithm is mainly divided into three parts: the first part is the CNN basic network, which is used to complete the extraction of image features; the second part is the RPN (Region Proposal Networks) network, which mainly uses the convolutional neural network to directly generate regions target, the method used is essentially a sliding window. A fixed-size window is used to slide on the last layer feature map of the CNN basic network, and each window will outp...

Embodiment 2

[0079] Such as figure 2 As shown, this embodiment proposes a hazard identification system for hoisting process based on deep learning, including:

[0080] The training module is used to train and generate Faster R-CNN workers and hook detection network;

[0081] In order to improve the efficiency of hazard identification in the hoisting process, the present invention uses Faster R-CNN to detect workers and hooks. The Faster RCNN network is one of the more popular general multi-target detection frameworks. The existing Faster RCNN algorithm is mainly divided into three parts: the first part is the CNN basic network, which is used to complete the extraction of image features; the second part is the RPN (Region Proposal Networks) network, which mainly uses the convolutional neural network to directly generate regions target, the method used is essentially a sliding window. A fixed-size window is used to slide on the last layer feature map of the CNN basic network, and each wi...

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Abstract

The invention discloses a hoisting process danger identification method and system based on deep learning, and the method comprises the steps: S1, training to generate a Faster R-CNN worker and lifting hook detection network; S2, detecting a worker who correctly wears the safety helmet, a worker who does not correctly wear the safety helmet and a lifting hook in the current frame image in the video shot by the lifting site by utilizing the detection network; S3, judging whether a worker is detected in the current frame image or not, and if so, executing the step S4; if not, extracting the nextframe of image of the shot video, and continuing to execute the step S2; s4, judging whether a person is allowed to enter the hoisting site or not, and if not, triggering an alarm; if so, executing the step S5; s5, judging whether a worker who does not correctly wear the safety helmet is detected in the current frame image or not, and if so, triggering an alarm; if not, executing the step S6; s6,generating paths of the workers and the lifting hooks based on the detected positions of the workers and the lifting hooks; and S7, judging whether the distance between the worker and the lifting hook is less than a preset distance at a certain moment in the worker and lifting hook prediction path, and if so, triggering an alarm. The danger in the hoisting process is comprehensively and timely recognized, and hoisting accidents can be effectively prevented.

Description

technical field [0001] The invention relates to the technical field of civil engineering construction machinery safety, in particular to a method and system for hazard identification in a hoisting process based on deep learning. Background technique [0002] With the continuous increase of urban population, the demand for high-rise and super high-rise buildings is increasing. In order to improve construction mechanization and industrialization, tower cranes are widely used in the construction process of high-rise and super high-rise buildings. According to incomplete statistics, among all kinds of special types of work, the number of hoisting accidents is almost the highest. The consequences of the accident are very important, and the proportion of serious injuries and deaths is also high, which has caused great concern. [0003] At present, hoisting safety monitoring mainly uses digital cameras to determine the location of construction workers, or uses statically placed and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08G06Q10/04
CPCG06Q10/04G06N3/08G06V20/40G06V20/44G06N3/045
Inventor 董梅张亮胡辉宋杰
Owner 杭州鲁尔物联科技有限公司
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